Zhengjun Li^{1}, Marta Vidorreta^{1}, Daniel Wolf^{2}, and John A. Detre^{1}

We used latent state-trait theory to examine trait specificity of regional cerebral blood flow (CBF) acquired using arterial spin labeled (ASL) perfusion MRI in four different resting-state conditions (eyes-open, eyes-closed, fixation, and a low-level attention task (psychomotor vigilance task, PVT)). Most brain regions accepted the latent state-trait model. Fixation exhibited the lowest latent trait specificity, while PVT, eyes-open, and eyes-closed showed progressively higher trait specificity. We confirmed that ASL CBF shows trait-like properties, which are optimally fit using eyes-open or eyes-closed conditions.

**PURPOSE**

Arterial
spin labeled (ASL) perfusion MRI can reliably quantify region cerebral blood
flow (CBF), and hence map regional brain function based on the tight coupling
between regional CBF and neural activity^{1,2}. A key feature of ASL MRI is its white noise
properties^{3}, which provides
sensitivity to state (low frequency) and trait (zero frequency) effects in
regional brain function. While ASL MRI
has been successfully used to identify trait-like properties of regional brain
function^{4,5}, the optimal experimental condition for detecting trait-effects
remains unknown.

Latent
state-trait theory (LST) has been applied in various domains, especially psychology^{6}. LST decomposes observable
variables into the sum of latent trait variables (personal attributes), state residuals
(person-situation interaction) and measurement errors^{6}. A previous study using
1.5T ASL data demonstrated that the mean CBF of various brain regions of
interest accepted the LST model^{7}.

In this study, we aimed to compare the trait specificity of ASL MRI data acquired using varying resting conditions: fixation, eyes-open, eyes-closed, and a low level attention task (psychomotor vigilance task, PVT).

**METHODS**

24
subjects (14 female, age = 29.0 ± 5.2 years) consented to the study and
completed two sessions spanning a mean of 7 days, conducted at the same time of
day for each subject, using either a Siemens Trio and 32-channel array (N=14)
or a Siemens Prisma and 64 channel array (N=10). The scanning protocol included
1mm isotropic T1-MPRAGE, TOF angiography to guide labeling plane selection, and
four 8-minute ASL scans obtained with ‘eyes-open’, ‘eyes-closed’, ‘fixation’,
and ‘PVT’ carried out in pseudorandomized order across subjects and sessions. A
single-shot, background-suppressed pseudocontinuous ASL (pCASL) sequence using
an accelerated 3D RARE Stack-Of-Spirals readout^{8} was employed to collect
whole-brain perfusion maps at 3.75mm isotropic resolution. An M0 image with no
ASL preparation and long TR was collected at the middle and end of the
sessions. Raw ASL images were realigned and registered to the anatomical
dataset using FSL^{9} and custom scripts in
Matlab. Subtracted pairs were converted into CBF units following the
one-compartment model^{10}.

LST
models were implemented with R toolbox “lsttheory”^{6} and “lavaan”^{11}.
For LST modeling, the
8-minute ASL scan for each condition was divided in two and the mean CBF maps were
calculated for each half. Both ROI-based (Table 1) and whole brain voxel-based
LST models (Fig. 1a) were estimated. A restrictive LST model^{7} equating the measurement
error variance, state residual variance, and effects of trait and states in the
model was used. Trait specificity was calculated for each model using the
following equation^{6}:

Trait Specificity = Variance of Trait / Variance of CBF

A
single grouped LST model (Fig. 1b) was additionally used to compare the trait
effects between the four conditions, combining the CBF measurements of the four
conditions as four groups and more stringently restricting the model to have
equal measurement error variance and equal state residual variance between the
groups. An LST model was only accepted when the
estimated model parameters were statistically significant (α = 0.05) and the discrepancy
between the model and the data (assessed by Chi-square test) was not
significant (α = 0.05)^{7}.

**RESULTS**

**DISCUSSION AND CONCLUSION**

1 Detre, J. A., Wang, J., Wang, Z. & Rao, H. Arterial spin-labeled perfusion MRI in basic and clinical neuroscience. Curr Opin Neurol 22, 348-355, doi:10.1097/WCO.0b013e32832d9505 (2009).

2 Chen, Y., Wang, D. J. & Detre, J. A. Test-retest reliability of arterial spin labeling with common labeling strategies. J Magn Reson Imaging 33, 940-949, doi:10.1002/jmri.22345 (2011).

3 Aguirre, G., Detre, J., Zarahn, E. & Alsop, D. Experimental design and the relative sensitivity of BOLD and perfusion fMRI. Neuroimage 15, 488-500 (2002).

4 Rao, H. et al. Genetic variation in serotonin transporter alters resting brain function in healthy individuals. Biological psychiatry 62, 600-606 (2007).

5 Kaczkurkin, A. N. et al. Elevated Amygdala Perfusion Mediates Developmental Sex Differences in Trait Anxiety. Biological psychiatry (2016).

6 Steyer, R., Mayer, A., Geiser, C. & Cole, D. A. A theory of states and traits--revised. Annu Rev Clin Psychol 11, 71-98, doi:10.1146/annurev-clinpsy-032813-153719 (2015).

7 Hermes, M. et al. Latent state-trait structure of cerebral blood flow in a resting state. Biol Psychol 80, 196-202, doi:10.1016/j.biopsycho.2008.09.003 (2009).

8 Vidorreta, M., Wang, Z., Chang, Y. V., Fernández-Seara, M. A. & Detre, J. A. Single-Shot Whole-Brain Background-Suppressed PCASL MRI with 1D Accelerated 3D RARE Stack-OfSpirals Readout. ISMRM Conference Proceedings (2015).

9 Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782-790, doi:10.1016/j.neuroimage.2011.09.015 (2012).

10 Detre, J. A., Leigh, J. S., Williams, D. S. & Koretsky, A. P. Perfusion imaging. Magnetic resonance in medicine 23, 37-45 (1992).

11 Rosseel, Y. lavaan: An R package for structural equation modeling. Journal of Statistical Software 48, 1-36 (2012).

Table 1: List of regions of interest (ROIs).

Fig. 1 Latent State-trait model

a).The Latent State-trait model (LST)^{6} decomposes the parallel measured CBF_{ik} (*i*th measure at occasion k) into measurements errors (err_{ik}) and latent state_{k}. It then further decomposes the latent state_{k} into state residual_{k} and the trait. The measurement error, the state residual and the trait are un-correlated with each other. A restrictive LST model used in this study further assumes equal measurement error variance and equal state residual variance. b). the grouped LST model nests four models for the four conditions and assumes same measurement error variance and same state residual variance across the conditions.

Fig. 2 Trait specificity of four resting conditions: region of interest based analysis results

Mean CBF measurements were analyzed separately using the Latent state-trait (LST) model for each of the resting conditions (each column) and each region of interest (ROI, each row). The colored grids are the regions that accepted the restrictive LST model (equal measurement error variance for the measurements, equal state residual variance across the occasions). The labeled values in the grids are the trait specificity of the CBF measures estimated by the LST model for the corresponding condition and ROI.

Fig. 3 Trait specificity of four resting conditions: Voxel-wise analysis results

The Latent state-trait (LST) model was estimated for each voxel’s CBF measures for each resting conditions (a, fixation; b, eyes-open; c, eyes-closed; d, PVT). The colored voxels are the voxels that accepted the restrictive LST model (equal measurement error variance for the measurements, equal state residual variance across the occasions). The color is showing the estimated trait specificity of the voxels. The CBF maps were smoothed with 6mm FWHM kernel before the model estimation for each voxel.

Fig. 4 Trait specificity of four resting conditions analyzed in single grouped model: ROI based analysis

The mean CBF measurements of the four resting conditions (the columns) were analyzed using a single grouped Latent state-trait (LST) model for each region of interest (ROI, each row). The colored grids are the regions that accepted the restrictive LST model (equal measurement error variance for all measurements, equal state residual variance across the occasions and resting conditions). The labeled values in the grids are the trait specificity of the CBF measures estimated by the LST model for the corresponding condition and ROI.